Thinking about what could lie ahead when climate-induced storms the strength of Hurricane Maria—the deadliest storm to hit Puerto Rico in almost a century—strike again keeps Maria Uriarte up at night.
“We have to prepare to minimize the damage of increasingly severe weather,” said Uriarte, a professor in Columbia’s Department of Ecology, Evolution and Environmental Biology. “Two years ago, Hurricane Maria destroyed thousands of acres of rainforests in Puerto Rico. But not all species were damaged. There were winners and losers—those that were resistant to storms and others that were not.”
Uriarte, who has been studying Puerto Rico’s trees for 15 years, said new tools are needed to predict the impact of a warming climate on forests. Not only do trees provide physical barriers against severe weather, they remove large amounts of carbon dioxide from the atmosphere, incorporating it through photosynthesis into their tissues as they grow.
“The challenge is to determine what impact past severe weather patterns have had on hurricane-resilient species,” Uriarte said.
As machine learning applications from big tech companies permeate increasingly large swaths of our lives, artists like Holly Herndon, YACHT, and Dadabots are using similar tools for their creative potential, crafting forward-looking albums in collaboration with artificial intelligence. Can they liberate A.I. from the banal and sinister world of email auto-complete suggestions and facial recognition software? And what will the music sound like if they do?
The video of Christiane’s daughter was promoted by YouTube’s systems months after the company was alerted that it had a pedophile problem. In February, Wired and other news outlets reported that predators were using the comment section of YouTube videos with children to guide other pedophiles.
That month, calling the problem “deeply concerning,” YouTube disabled comments on many videos with children in them.
It could be that Richard Socher’s operating system just runs with more energy than other people’s. He has just flown in from California and his body clock is telling him it’s still 4 a.m. Already, though, he has delivered a keynote address, participated in a panel and held a question-and-answer session at the START Summit in St. Gallen, Switzerland, an important innovation conference.
Despite all that, he’s in a good mood as he poses for the ZEIT ONLINE photographer and later helps carry her flash equipment. He then sits down in a drafty corner of the congress hall for the following three-hour interview. After an hour, he remembers that he hasn’t yet eaten today. He has a breakfast of Red Bull and a ham and cheese croissant.
Journal of the American Medical Informatics Association, Mitchell R. Lunn et al.
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Materials and Methods
We partnered with design and development firms and engaged SGM community members to build a secure, cloud-based, containerized, microservices-based, feature-rich, research platform. We created PRIDEnet, a national network of individuals and organizations that actively engaged SGM communities in all stages of health research. The PRIDE Study participants were recruited via in-person outreach, communications to PRIDEnet constituents, social media advertising, and word-of-mouth. Participants completed surveys to report demographic as well as physical, mental, and social health data. Results
We built a secure digital research platform with engaging functionality that engaged SGM people and recruited and retained 13 731 diverse individuals in 2 years. A sizeable sample of 3813 gender minority people (32.8% of cohort) were recruited despite representing only approximately 0.6% of the population. Participants engaged with the platform and completed comprehensive annual surveys— including questions about sensitive and stigmatizing topics— to create a data resource and join a cohort for ongoing SGM health research. Discussion
With an appealing digital platform, recruitment and engagement in online-only longitudinal cohort studies are possible. Participant engagement with meaningful, bidirectional relationships creates stakeholders and enables study cocreation. Research about effective tactics to engage, recruit, and maintain active participation from all communities is needed. Conclusion
This digital research platform successfully recruited and engaged diverse SGM participants in The PRIDE Study. A similar approach may be successful in partnership with other underrepresented and vulnerable populations.
Several Chinese graduate students and academics told Bloomberg News in recent weeks that they found the U.S. academic and job environment increasingly unfriendly. Emory University dismissed two Chinese-American professors on May 16, and China’s Education Ministry issued a warning Monday on the risks of studying in the U.S. as student visa rejections soar.
Nature Ecology & Evolution; Miguel F. Jimenez, Theresa M. Laverty, Sara P. Bombaci, Kate Wilkins, Drew E. Bennett & Liba Pejchar
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A diverse and inclusive scientific community is more productive, innovative and impactful, yet ecology and evolutionary biology continues to be dominated by white male faculty. We quantify faculty engagement in activities related to diversity and inclusion and identify factors that either facilitate or hinder participation. Through a nationwide survey, we show that faculty with underrepresented identities disproportionally engage in diversity and inclusion activities, yet such engagement was not considered important for tenure. Faculty perceived time and funding as major limitations, which suggests that institutions should reallocate resources and reconsider how faculty are evaluated to promote shared responsibility in advancing diversity and inclusion.
Arturo grew out of investment and more than three years of research by American Family Insurance on the application of artificial intelligence (AI) and deep learning to satellite, aerial, drone, and ground-level imagery to accurately assess physical property characteristics for residential and commercial properties.
Arturo’s AI-powered analytics generate detailed property information often in under five seconds. It enables a variety of businesses that insure, lend, invest, or manage residential or commercial properties to make more informed decisions and better manage risk with the most up to date information available.
What’s most compelling about Arturo AI, is that it was created from within an insurance company with product development experts and claims and underwriting professionals giving feedback as the technology was built. That work behind-the-scenes, in a test-and-learn environment with American Family and several leading insuretech startups, distinguishes it from similar companies.
The Urban Institute; Diana Elliott, Charmaine Runes Rob Santos & Steven Martin
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The 2020 Census faces unprecedented challenges and threats to its accuracy. Demographic changes over the past decade will make the population harder to count. And underfunding, undertested process changes, and the last-minute introduction of a citizenship question could result in serious miscounts, potentially diminishing communities’ rightful political voice and share of funding.
To understand how these factors could affect the 2020 Census counts, we created projections under three scenarios—reflecting the miscount risk as low, medium, or high.
When Knowles and the more business-minded Toon founded Graphcore in 2016, they put “less precise” computing at the heart of their chips, which they call intelligence processing units, or IPUs. “The concepts in your brain are quite vague. It’s really the aggregation of very approximate data points that causes you to have precise thoughts,” says Knowles, whose English accent and frequent chuckle invite comparisons to a Hogwarts headmaster. (Given his constant whiteboard pontificating, Toon jokingly addresses him as “Professor Knowles.”) There are various theories on why human intelligence forms this way, but for machine learning systems, which need to process huge and amorphous information structures known as “graphs,” building a chip that specializes in connecting nodelike data points may prove key in the evolution of AI. “We wanted to build a very high-performance computer that manipulates numbers very imprecisely,” Knowles says.
Given the past few years of history, I wasn’t expecting to hear much about HomeKit this week from Apple’s WorldWide Developer Conference. And yet Apple proved me wrong, announcing HomeKit in routers and more secure video from HomeKit-enabled webcams. Oh, and that video ties directly into Apple’s iCloud plans, which could generate more revenue for the company as well.
IoT devices on home networks have long been a concern of many homeowners and for good reason: It seems like once a month we hear about some security issue with certain IoT devices that could allow a nefarious third-party to access your home’s data or remotely control something in your home. Apparently, Apple decided to address this valid concern through the home router. Or more specifically, a home router from specific companies that are willing to work with Apple.
I’ve been a little frustrated with the state of the connected security market for a while now. After a spate of products such as Canary and Piper, which reconfigured the alarm system market with point products that use sensors integrated into a single hub to determine if a break-in has occurred, the market somewhat stagnated.
We still have a cluster of sensors on our doors, windows, and walls aimed at detecting when something opens, closes, moves, or breaks something inside the home. Amazon’s launch of the Alexa Guard glass break and smoke alarm detection feature as part of the Echo device represented a leap forward to that smarter security system, but it felt like a small leap.
Enter Minut, a five-year-old company based in Malmo, Sweden. The firm has built a device it calls Point that uses sensors and machine learning to determine if someone has broken into your home or to simply to let you know when something is wrong. Its most interesting feature is that it has done this in a way that protects the users’ privacy, even as it sends data to the cloud.
Consumer interest in DNA testing has exploded, helping to ignite a new “hybrid” model of genetic testing that blends elements of traditional laboratories with direct-to-consumer models (DTC) models like 23andMe, according to new research published in JAMA Network.
It’s tough to quantify what proportion of genetic tests is fulfilled via hybrid models versus traditional and DTC models, but data suggests hybrid labs are eating up a big chunk of genetic testing: A large traditional laboratory recently reported that it’s conducted 4 million genetic tests since launching in 1991, while a hybrid laboratory reported it’s conducted 1.4 million tests since its founding in 2004.
Hybrid DNA labs emerged to bridge the gap left by traditional and DTC genetic tests — which are centric to either the clinician or the consumer, not both.
Seattle, WA July 25. Theme: Open Science and the Role of Common Evidence. Young Investigators Award: “Help us include a new generation of open scientists at this event by encouraging early-career researchers to apply for our Young Investigators Award. Recipients will be featured at the event and will receive a stipend to cover travel and lodging.”
Austin, TX July 13-14. “The SciPy Conference dedicates the last two days of the week to push our ecosystem forward through developer sprints. It is an informal part of the conference, all about exchanging, hacking and creating. Everyone is welcome regardless of interest, need and programming level.” [registration required]
“Johns Hopkins APL is recruiting people who love to solve puzzles, logic games, and analytic problems to participate in research studies exploring reasoning in groups.”
Many people started switching their Python versions from 2 to 3 as a result of Python EOL. Unfortunately, most Python 3 I find still looks like Python 2, but with parentheses (even I am guilty of that in my code examples in previous posts – Introduction to web scraping with Python). Below, I show some examples of exciting features you can only use in Python 3 in the hopes that it will make solving your problems with Python easier.
CERN has partnered with 10 multidisciplinary institutions and companies to build a turn-key open source research data management platform called Invenio RDM, and grow a diverse community to sustain the platform.